Many natural scenes have wider dynamic ranges than those that can be recorded by conventional low dynamic range (LDR) imaging systems. An LDR image with small exposure time is under-exposed in the dark scene regions but captures the bright areas well. In contrast, an LDR image with large exposure time is saturated in the bright scene regions but captures the dark areas well. In other words, one LDR image is not able to represent the whole scene. A high dynamic range (HDR) image can be produced to represent the whole scene by sequentially capturing multiple differently exposed LDR images using normal cameras.
One of the challenges of digital image processing is the generation of natural scene which possesses HDR image on a conventional LDR display. Two types of methods were proposed to address this challenge by processing multiple of differently exposed LDR images. The former, called HDR imaging, first generates an HDR image by using these LDR images, and then converts the HDR image into an LDR image so as to visualize the HDR scene via an LDR display. The latter, called exposure fusion, generates an LDR image directly by fusing all LDR images. Exposure fusion is much simpler than HDR imaging, and is more suitable for smart phones and digital cameras where complexity can be an issue. Furthermore, exposure fusion does not require lighting conditions of all images to be the same as required by HDR imaging. Therefore, exposure fusion is more attractive from mobile application point of view.
In exposure fusion of differently exposed LDR images, extraction of fine details is required. Many other applications, such as de-noising of images, tone mapping of HDR images, detail enhancement via, multi-light images and so on, requires extraction of fine details as well.
Fine details can be either noise, e.g. a random pattern with zero mean, or texture, e.g. a repeated pattern with regular structure. There are many methods for extraction of fine details from a single input image, such as the total variation based method, the half quadratic optimization based method, the bilateral filter, etc. However, when there are multiple input images, using these methods directly would be complex because each input image needs to be decomposed individually.
Recently, a quadratic optimization based framework is proposed which can be adopted to extract fine details from a set of images simultaneously. The quadratic optimization problem is solved by using an iterative method which may be complex for mobile devices with limited computational resources. Given the increasing popularity of multi-shot imaging and the intense response from camera manufacturers with their latest digital camera for multi-shot capability, it is desirable to provide a better solution that extracts optimally captured details from a number of individual photographs and integrates them together to form a better image.